结核病的诊断，尤其是肺外结核的诊断，在临床实践中仍然面临挑战。这有几个原因。基于结核分枝杆菌(Mtb)检测的方法灵敏度不够,基于Mtb特异性免疫反应检测的方法不能总是区分活动性疾病和潜伏感染，Mtb感染的一些血清学标记物的特异性不足以区分结核病和其他炎症性疾病。基于流式细胞术等技术的新工具，质谱,高通量测序，而人工智能有可能解决这一困境。这篇综述的目的是提供当前优化经典诊断方法的最新概述。以及新的分子和其他方法，对Mtb感染患者进行准确诊断。
Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection.
